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Data Preparation and Overview

Setup

Standard packages

library(here)
source(here("code", "standard_libraries.R"))

Additional Packages

suppressPackageStartupMessages({
library(DropletUtils)
library(scDblFinder)
})

Set parameter

set.seed(123)
bpp <- BiocParallel::MulticoreParam(parallel::detectCores()-1, RNGseed=123)
path <- here::here()

Cellranger Sample Metrics

Read in the cellranger sample metrics csv files

foldernames <- list.dirs(paste0(path,"/data/cellranger_data_h5") ,recursive = F, full.names = T)

CR.metrics <- lapply(foldernames, function(i){
  # remove _Counts is if names don't include them
  metrics <- read.csv(file.path(paste0(i),"metrics_summary.csv"), colClasses = "character")
})
experiment.metrics <- do.call("rbind", CR.metrics)
rownames(experiment.metrics) <- list.dirs(paste0(path,"/data/cellranger_data_h5") ,recursive = F, full.names = F)

# Rename some rows/cols
colnames(experiment.metrics) <- gsub("\\."," ", colnames(experiment.metrics))
rownames(experiment.metrics) <-sub("\\_.*","\\1",rownames(experiment.metrics))

# Print table
experiment.metrics

Load Data

Load Cellbender output matrices

Load in the filtered feature barcode matrices from Cellbender of each sample and save them as sce object.

#Load Dataset
rawdata_folder <- paste0(path,"/data/cellbender_data_h5/")

#Get file names
filenames <- list.files(rawdata_folder ,recursive = F, full.names = F,pattern = "\\_filtered.h5$")
filepaths <- paste(rawdata_folder,filenames,sep = "")

#Load data as sce object
sce<-read10xCounts(samples = filepaths, sample.names = filenames,col.names=T,type="HDF5")

#Edit Filenames 
colData(sce)$Sample = sub("\\_w_introns.*", "", sub("cellbender_output_", "", as.character(colData(sce)$Sample)))


sce
class: SingleCellExperiment 
dim: 36601 119652 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(3): ID Symbol Type
colnames(119652): 1_AAGTACCGTCTCTCAC-1 1_GACTCTCAGGTAACTA-1 ...
  17_AGGTGTTGTGGCATCC-1 17_GCCAGCACACAAGTGG-1
colData names(2): Sample Barcode
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

Load and add Metadata

#Metadata path
metadata_folder <- paste0(path,"/data/metadata/Metadata_Master.csv")

#Load metadata
metadata_df <-read.csv(metadata_folder, header=TRUE, sep=",")
metadata_df
#Add metadata to sce
colData(sce) <- dplyr::left_join(as.data.frame(colData(sce)),
                                   metadata_df, 
                                   by= c("Sample" = "Sample"),
                                   suffix=c(".x",".y")) %>% 
      #dplyr::select(Sample, Barcode, Diagnosis, sex, Age, Joint.Location, protocol, Pathotype,Krenn total score,) %>% 
      dplyr::select(-one_of("Comments")) %>% #select all except
      DataFrame(row.names=colnames(sce))

#Set Sample names
names(colData(sce))[which(names(colData(sce))=="Sample")]="Orig.Identifier"
names(colData(sce))[which(names(colData(sce))=="ID")]="Sample"

#make col names unique
colnames(sce) <- paste0(sce$Sample, ".", sce$Barcode)

Annotate and Update genes

#Change Name
names(rowData(sce))[which(names(rowData(sce))=="ID")]="ENSEMBL"

#Annotate genes
AnnoGene <- annotate_genes(data.frame(rowData(sce)), gene_col = "Symbol")
#Add Annotated and Updated genes
rowData(sce) <- AnnoGene

#Make row and col names unique
rownames(sce) <- paste0(rowData(sce)$ENSEMBL, ".", rowData(sce)$Symbol)

Dimensions of the count matrix

#Dimensions of count matrix
dim(sce)
[1]  36601 119652

Explore dataset

#Feautures/row data
data.frame(colData(sce))
#Droplet details / row data
data.frame(rowData(sce))

Exclude Samples

sce <- sce[,sce$Sample != "SynBio_130"]
dim(sce)
[1]  36601 110640

Exploratory plots

Histogramm with number of cells

Show the number of cells detected in each sample or joint location before filtering

Per Sample

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Sample))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample") + 
  theme_classic()+
  stat_count(geom = "text", colour = "white", size = 3.5,aes(label = ..count..),position=position_stack(vjust=0.65))+
  theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())+
  scale_y_continuous(expand = c(0,0))

Version Author Date
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
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d291fd3 sarloet 2024-04-02
data.frame(as.list(table(colData(sce)$Sample)))

Per Joint Location

#Histogramm with number of cells per Joint
ggplot(colData(sce), aes(x=Joint.Location))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per Joint") + 
  theme_classic()+
  stat_count(geom = "text", colour = "white", size = 3.5,aes(label = ..count..),position=position_stack(vjust=0.8))+
  theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())+
  scale_y_continuous(expand = c(0,0))

Version Author Date
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
d18442a sarloet 2024-05-06
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data.frame(as.list(table(colData(sce)$Joint.Location)))

Plot number of genes detected per cell

This plot shows cell counts per sample / count occurrence

#Number of genes detected per cell
#Total UMI for a gene versus the number of times detected
genesPerCell <- colSums(counts(sce) > 0)
plot(density(genesPerCell), 
     xlab="Genes per cell", 
     main="Number of genes detected per cell")

Version Author Date
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
d18442a sarloet 2024-05-06
984bfbe sarloet 2024-05-02
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d291fd3 sarloet 2024-04-02

Plot transcript capture efficiency

This plot gives an idea about the sequencing depth and if the sequencing has reached saturation or not. Plotted is the total gene count across all cells (x-axis) vs Proportion of cells the gene is detected in (y-axis) where each dot represents a gene.

#transcript_capture_efficiency
#Total UMI for a gene versus the number of times detected
tmpCounts <- counts(sce)

plot(rowSums(tmpCounts),
     rowMeans(tmpCounts > 0),
     log = "x",
     xlab="total number of UMIs",
     ylab="proportion of cells expressing the gene",
     main="Total UMI for a gene vs times detected")

Version Author Date
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
d18442a sarloet 2024-05-06
984bfbe sarloet 2024-05-02
b1fc000 sarloet 2024-04-19
5067fa6 sarloet 2024-04-04
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Initial Filtering

Initial filtering to remove unexpressed genes and cells with very low number of counts. The input to scDblFinder should not include empty droplets, and it might be necessary to remove cells with a very low coverage (e.g. <200 reads) to avoid errors. Further quality filtering should be performed downstream of doublet detection.

#Initial filtering before droplet removal
dim_before_filtering <- dim(sce)

#Get only the detected Genes
#Remove Cells with very low counts of less than 150 and genes not expressed 
sce <- sce[rowSums(counts(sce)> 0) > 0, colSums(counts(sce)> 0) > 150]


dim_after_filtering <- dim(sce)

#Give Stats
cat("NR of Cells Before Initial Filtering: ", dim_before_filtering[2],"\n",
    "NR of Cells After Initial Filtering: ", dim_after_filtering[2],"\n",
    "NR of Cells Filtered out: ", dim_before_filtering[2] - dim_after_filtering[2],"\n",
    "Cells Filtered out: [%]", (dim_before_filtering[2] - dim_after_filtering[2])/dim_before_filtering[2]*100,"\n",
    "\n",
    "NR of Genes Before Initial Filtering: ", dim_before_filtering[1],"\n",
    "NR of Genes After Initial Filtering: ", dim_after_filtering[1],"\n",
    "NR of Genes Filtered out: ", dim_before_filtering[1] - dim_after_filtering[1],"\n",
    "Genes Filtered out: [%]", (dim_before_filtering[1] - dim_after_filtering[1])/dim_before_filtering[1]*100,"\n"
    )
NR of Cells Before Initial Filtering:  110640 
 NR of Cells After Initial Filtering:  84318 
 NR of Cells Filtered out:  26322 
 Cells Filtered out: [%] 23.79067 
 
 NR of Genes Before Initial Filtering:  36601 
 NR of Genes After Initial Filtering:  34550 
 NR of Genes Filtered out:  2051 
 Genes Filtered out: [%] 5.603672 

Doublet Detection

Doublets are defined as two cells that are sequenced under the same cellular barcode, which happens if they were captured in the same droplet. The scDblFinder method combines the strengths of various doublet detection approaches, training an iterative classifier on the neighborhood of real cells and artificial doublets. Doublet removal is performed on feature-barcode matrix after a initial filtering to remove Cells with very low counts of less than 200 counts and genes not expressed to ensure no potential empty droplets.

Detection

#Detection
sce <- scDblFinder::scDblFinder(sce, samples=sce$Sample, BPPARAM = bpp) 

table(sce$scDblFinder.class)

singlet doublet 
  76772    7546 
as.data.frame.matrix(table(sce$Sample,sce$scDblFinder.class))

Doublet Detection Plots

Singlet/Doublet Histogramm

Absolute comparison
#Plot singlet/doublet histogramm Absolute comparison / by sample
as.data.frame(colData(sce)) %>%
  dplyr::group_by(Sample, scDblFinder.class) %>%
  dplyr::summarise(Freq=n()) %>%
  ggplot(aes(x=Sample, y=Freq, fill=scDblFinder.class,label=Freq)) + 
    geom_bar(stat="identity") +
    labs(title="Doublet detection results",
    subtitle="By Sample",x="",y="Number of Cells") +
    geom_text(size=3, position = position_stack(vjust=0.5))+
    theme(axis.text.x = element_text(angle = 45,hjust=1), axis.line = element_line(colour = "black"), panel.background = element_rect(fill = NA))+
    scale_y_continuous(expand = c(0,0)) + 
    scale_fill_manual(values = c("#4dc8c9","#FB8072"))

Version Author Date
e3ca53c sarloet 2024-06-01
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
ed4a9ca sarloet 2024-05-21
db19512 sarloet 2024-05-17
d18442a sarloet 2024-05-06
Relative comparison
# Plot singlet/doublet histogramm Relative comparison / by sample
as.data.frame(colData(sce)) %>%
  dplyr::group_by(Sample, scDblFinder.class) %>%
  dplyr::summarise(Freq=n()) %>% 
  ggplot(aes(x=Sample, y=Freq, fill=scDblFinder.class, label=Freq)) +
    geom_bar(stat="identity", position="fill") +
    labs(title="Doublet detection results",
       subtitle="By Sample",
       x="Sample",
       y="Number of cells") + 
    geom_text(size=2.5, position = position_fill(vjust=0.5)) +
    theme(axis.text.x = element_text(angle = 45,hjust=1), axis.line = element_line(colour = "black"), panel.background = element_rect(fill = NA))+
    scale_y_continuous(expand = c(0,0))+ 
    scale_fill_manual(values = c("#4dc8c9","#FB8072"))

Version Author Date
e3ca53c sarloet 2024-06-01
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
ed4a9ca sarloet 2024-05-21
db19512 sarloet 2024-05-17
d18442a sarloet 2024-05-06

Singlet/Doublet Scatter

sce <- addPerCellQC(sce)

#Plot singlet/doublet qcplots
colData(sce) %>% 
    as.data.frame() %>% 
    arrange(scDblFinder.class) %>% 
    ggplot(aes(x = sum, y = detected )) +
      geom_point(aes(colour = scDblFinder.class),size=0.5) + 
      facet_wrap(vars(Sample))+ 
      labs(title="Total number of detected genes plotted against total number of UMIs",
       x="Total counts",
       y="Detected genes") +
      theme(strip.background=element_rect(fill="white"), panel.background = element_rect(fill = NA),axis.line = element_line(colour = "black"))+ 
    scale_fill_manual(values = c("#FB8072","#4dc8c9"))

Version Author Date
e3ca53c sarloet 2024-06-01
4b8dd48 sarloet 2024-05-28
fe7615b sarloet 2024-05-28
906ed8c sarloet 2024-05-23
ed4a9ca sarloet 2024-05-21
db19512 sarloet 2024-05-17
d18442a sarloet 2024-05-06

Apply Doublet Removal

#Doublet Removal

dim_before_doublet <- dim(sce)

#Apply Doublet Removal
sce <- sce[ ,sce$scDblFinder.class == "singlet"]

dim_after_doublet <- dim(sce)


#Give Stats
cat("NR of Cells Before Doublet Removal: ", dim_before_doublet[2],"\n",
    "NR of Cells After Doublet Removal: ", dim_after_doublet[2],"\n",
    "NR of Cells Filtered out: ", dim_before_doublet[2] - dim_after_doublet[2],"\n",
    "Cells Filtered out: [%]", (dim_before_doublet[2] - dim_after_doublet[2])/dim_before_doublet[2]*100,"\n"
    )
NR of Cells Before Doublet Removal:  84318 
 NR of Cells After Doublet Removal:  76772 
 NR of Cells Filtered out:  7546 
 Cells Filtered out: [%] 8.949453 

Save the dataset

saveRDS(sce, file =paste0(path,'/output/00_sce_DataPreparation.rds'))

sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Warsaw
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods  
[8] base     

other attached packages:
 [1] scDblFinder_1.16.0          DropletUtils_1.22.0        
 [3] tidyr_1.3.1                 org.Hs.eg.db_3.18.0        
 [5] AnnotationDbi_1.64.1        clusterProfiler_4.10.1     
 [7] viridis_0.6.5               viridisLite_0.4.2          
 [9] gridExtra_2.3               scran_1.30.2               
[11] scater_1.30.1               scuttle_1.12.0             
[13] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[15] Biobase_2.62.0              GenomicRanges_1.54.1       
[17] GenomeInfoDb_1.38.8         IRanges_2.36.0             
[19] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[21] MatrixGenerics_1.14.0       matrixStats_1.3.0          
[23] dplyr_1.1.4                 ggplot2_3.5.1              
[25] BiocParallel_1.36.0         here_1.0.1                 
[27] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] splines_4.3.3             later_1.3.2              
  [3] BiocIO_1.12.0             bitops_1.0-7             
  [5] ggplotify_0.1.2           R.oo_1.26.0              
  [7] tibble_3.2.1              polyclip_1.10-6          
  [9] XML_3.99-0.16.1           lifecycle_1.0.4          
 [11] edgeR_4.0.16              rprojroot_2.0.4          
 [13] processx_3.8.4            lattice_0.22-5           
 [15] MASS_7.3-60.0.1           magrittr_2.0.3           
 [17] limma_3.58.1              sass_0.4.9               
 [19] rmarkdown_2.27            jquerylib_0.1.4          
 [21] yaml_2.3.8                metapod_1.10.1           
 [23] httpuv_1.6.15             cowplot_1.1.3            
 [25] DBI_1.2.2                 RColorBrewer_1.1-3       
 [27] abind_1.4-5               zlibbioc_1.48.2          
 [29] R.utils_2.12.3            purrr_1.0.2              
 [31] ggraph_2.2.1              RCurl_1.98-1.14          
 [33] yulab.utils_0.1.4         tweenr_2.0.3             
 [35] git2r_0.33.0              GenomeInfoDbData_1.2.11  
 [37] enrichplot_1.22.0         ggrepel_0.9.5            
 [39] irlba_2.3.5.1             tidytree_0.4.6           
 [41] dqrng_0.4.0               DelayedMatrixStats_1.24.0
 [43] codetools_0.2-19          DelayedArray_0.28.0      
 [45] DOSE_3.28.2               ggforce_0.4.2            
 [47] tidyselect_1.2.1          aplot_0.2.2              
 [49] farver_2.1.2              ScaledMatrix_1.10.0      
 [51] GenomicAlignments_1.38.2  jsonlite_1.8.8           
 [53] BiocNeighbors_1.20.2      tidygraph_1.3.1          
 [55] tools_4.3.3               treeio_1.26.0            
 [57] Rcpp_1.0.12               glue_1.7.0               
 [59] SparseArray_1.2.4         xfun_0.44                
 [61] qvalue_2.34.0             HDF5Array_1.30.1         
 [63] withr_3.0.0               BiocManager_1.30.23      
 [65] fastmap_1.2.0             rhdf5filters_1.14.1      
 [67] bluster_1.12.0            fansi_1.0.6              
 [69] callr_3.7.6               digest_0.6.35            
 [71] rsvd_1.0.5                R6_2.5.1                 
 [73] gridGraphics_0.5-1        colorspace_2.1-0         
 [75] GO.db_3.18.0              RSQLite_2.3.6            
 [77] R.methodsS3_1.8.2         utf8_1.2.4               
 [79] generics_0.1.3            renv_1.0.7               
 [81] data.table_1.15.4         rtracklayer_1.62.0       
 [83] graphlayouts_1.1.1        httr_1.4.7               
 [85] S4Arrays_1.2.1            scatterpie_0.2.2         
 [87] whisker_0.4.1             pkgconfig_2.0.3          
 [89] gtable_0.3.5              blob_1.2.4               
 [91] XVector_0.42.0            shadowtext_0.1.3         
 [93] htmltools_0.5.8.1         fgsea_1.28.0             
 [95] scales_1.3.0              png_0.1-8                
 [97] ggfun_0.1.4               knitr_1.45               
 [99] rstudioapi_0.16.0         rjson_0.2.21             
[101] reshape2_1.4.4            nlme_3.1-164             
[103] rhdf5_2.46.1              cachem_1.1.0             
[105] stringr_1.5.1             parallel_4.3.3           
[107] vipor_0.4.7               HDO.db_0.99.1            
[109] restfulr_0.0.15           pillar_1.9.0             
[111] grid_4.3.3                vctrs_0.6.5              
[113] promises_1.3.0            BiocSingular_1.18.0      
[115] beachmat_2.18.1           cluster_2.1.6            
[117] beeswarm_0.4.0            evaluate_0.23            
[119] Rsamtools_2.18.0          cli_3.6.2                
[121] locfit_1.5-9.9            compiler_4.3.3           
[123] rlang_1.1.3               crayon_1.5.2             
[125] labeling_0.4.3            ps_1.7.6                 
[127] getPass_0.2-4             plyr_1.8.9               
[129] fs_1.6.4                  ggbeeswarm_0.7.2         
[131] stringi_1.8.4             munsell_0.5.1            
[133] Biostrings_2.70.3         lazyeval_0.2.2           
[135] GOSemSim_2.28.1           Matrix_1.6-5             
[137] patchwork_1.2.0           sparseMatrixStats_1.14.0 
[139] bit64_4.0.5               Rhdf5lib_1.24.2          
[141] KEGGREST_1.42.0           statmod_1.5.0            
[143] highr_0.10                igraph_2.0.3             
[145] memoise_2.0.1             bslib_0.7.0              
[147] ggtree_3.10.1             fastmatch_1.1-4          
[149] xgboost_1.7.7.1           bit_4.0.5                
[151] gson_0.1.0                ape_5.8